IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i8d10.1007_s10845-022-01994-1.html
   My bibliography  Save this article

Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review

Author

Listed:
  • Tongwha Kim

    (University of Toronto)

  • Kamran Behdinan

    (University of Toronto)

Abstract

With the high demand and sub-nanometer design for integrated circuits, surface defect complexity and frequency for semiconductor wafers have increased; subsequently emphasizing the need for highly accurate fault detection and root-cause analysis systems as manual defect diagnosis is more time-intensive, and expensive. As such, machine learning and deep learning methods have been integrated to automated inspection systems for wafer map defect recognition and classification to enhance performance, overall yield, and cost-efficiency. Concurrent with algorithm and hardware advances, in particular the onset of neural networks like the convolutional neural network, the literature for wafer map defect detection exploded with new developments to address the limitations of data preprocessing, feature representation and extraction, and model learning strategies. This article aims to provide a comprehensive review on the advancement of machine learning and deep learning applications for wafer map defect recognition and classification. The defect recognition and classification methods are introduced and analyzed for discussion on their respective advantages, limitations, and scalability. The future challenges and trends of wafer map detection research are also presented.

Suggested Citation

  • Tongwha Kim & Kamran Behdinan, 2023. "Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3215-3247, December.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:8:d:10.1007_s10845-022-01994-1
    DOI: 10.1007/s10845-022-01994-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-022-01994-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-022-01994-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cheng Hao Jin & Hyun-Jin Kim & Yongjun Piao & Meijing Li & Minghao Piao, 2020. "Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1861-1875, December.
    2. Byunghoon Kim & Young-Seon Jeong & Seung Hoon Tong & Myong K. Jeong, 2020. "A generalised uncertain decision tree for defect classification of multiple wafer maps," International Journal of Production Research, Taylor & Francis Journals, vol. 58(9), pages 2805-2821, May.
    3. Hsu, Shao-Chung & Chien, Chen-Fu, 2007. "Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 107(1), pages 88-103, May.
    4. Jinho Kim & Youngmin Lee & Heeyoung Kim, 2018. "Detection and clustering of mixed-type defect patterns in wafer bin maps," IISE Transactions, Taylor & Francis Journals, vol. 50(2), pages 99-111, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chia-Yu Hsu & Ju-Chien Chien, 2022. "Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 831-844, March.
    2. Chengjun Xu & Guobin Zhu, 2021. "Intelligent manufacturing Lie Group Machine Learning: real-time and efficient inspection system based on fog computing," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 237-249, January.
    3. Chen-Fu Chien & Hsin-Jung Wu, 2024. "Integrated circuit probe card troubleshooting based on rough set theory for advanced quality control and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 275-287, January.
    4. Tae San Kim & Jong Wook Lee & Won Kyung Lee & So Young Sohn, 2022. "Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1715-1724, August.
    5. Chen-Fu Chien & Chiao-Wen Liu & Shih-Chung Chuang, 2017. "Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5095-5107, September.
    6. Eduardo Oliveira & Vera L. Miguéis & José L. Borges, 2023. "Automatic root cause analysis in manufacturing: an overview & conceptualization," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2061-2078, June.
    7. Minghao Piao & Cheng Hao Jin, 2023. "CNN and ensemble learning based wafer map failure pattern recognition based on local property based features," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3599-3621, December.
    8. Dutta, Debprotim & Bose, Indranil, 2015. "Managing a Big Data project: The case of Ramco Cements Limited," International Journal of Production Economics, Elsevier, vol. 165(C), pages 293-306.
    9. Wenhan Fu & Chen-Fu Chien & Lizhen Tang, 2022. "Bayesian network for integrated circuit testing probe card fault diagnosis and troubleshooting to empower Industry 3.5 smart production and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 785-798, March.
    10. Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
    11. Chen-Fu Chien & Chung-Jen Kuo & Chih-Min Yu, 2020. "Tool allocation to smooth work-in-process for cycle time reduction and an empirical study," Annals of Operations Research, Springer, vol. 290(1), pages 1009-1033, July.
    12. Seyoung Park & Jaeyeon Jang & Chang Ouk Kim, 2021. "Discriminative feature learning and cluster-based defect label reconstruction for reducing uncertainty in wafer bin map labels," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 251-263, January.
    13. Feng Huang & Ben-wu Wang & Qi-peng Li & Jun Zou, 2023. "Texture surface defect detection of plastic relays with an enhanced feature pyramid network," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1409-1425, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:34:y:2023:i:8:d:10.1007_s10845-022-01994-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.